Bayesian cluster detection via adjacency modelling

Disease mapping aims to estimate the spatial pattern in disease risk across an area, identifying units which have elevated disease risk. Existing methods use Bayesian hierarchical models with spatially smooth conditional autoregressive priors to estimate risk, but these methods are unable to identify the geographical extent of spatially contiguous high-risk clusters of areal units. Our proposed solution to this problem is a two-stage approach, which produces a set of potential cluster structures for the data and then chooses the optimal structure via a Bayesian hierarchical model. The first stage uses a spatially adjusted hierarchical agglomerative clustering algorithm. The second stage fits a Poisson log-linear model to the data to estimate the optimal cluster structure and the spatial pattern in disease risk. The methodology was applied to a study of chronic obstructive pulmonary disease (COPD) in local authorities in England, where a number of high risk clusters were identified

On the difficulty to delimit disease risk hot spots

International audienceRepresenting the health state of a region is a helpful tool to highlight spatial heterogeneity and localize high risk areas. For ease of interpretation and to determine where to apply control procedures, we need to clearly identify and delineate homogeneous regions in terms of disease risk, and in particular disease risk hot spots. However, even if practical purposes require the delineation of different risk classes, such a classification does not correspond to a reality and is thus difficult to estimate. Working with grouped data, a first natural choice is to apply disease mapping models. We apply a usual disease mapping model, producing continuous estimations of the risks that requires a post-processing classification step to obtain clearly delimited risk zones. We also apply a risk partition model that build a classification of the risk levels in a one step procedure. Working with point data, we will focus on the scan statistic clustering method. We illustrate our article with a real example concerning the bovin spongiform encephalopathy (BSE) an animal disease whose zones at risk are well known by the epidemiologists. We show that in this difficult case of a rare disease and a very heterogeneous population, the different methods provide risk zones that are globally coherent. But, related to the dichotomy between the need and the reality, the exact delimitation of the risk zones, as well as the corresponding estimated risks are quite different

Identifying clusters in Bayesian disease mapping

This thesis develops statistical methodology for disease mapping, an increasingly important field of spatial epidemiology. Disease mapping has applications in public health by allowing for identification of areas which are at high risk of particular health problems. Such approaches are generally based on areal data, which involves partitioning the study region into a set of non-overlapping areal units and recording counts of disease cases within each areal unit. The majority of approaches assume a spatially smooth risk surface, but this may not be realistic, and there has been recent interest in developing methodology which allows for discontinuities in this structure. This can be done by identifying clusters of areal units with similar disease risks, and allowing for discontinuities between these clusters. The work presented in this thesis develops models to identify such clusters and also estimate disease risk. Three Bayesian hierarchical models are proposed; the first two are based on spatial data at a single time point, while the third extends into the spatio-temporal domain by modelling across multiple time points. Each model is applied to respiratory hospital admission data from the Greater Glasgow and Clyde Health Board area in order to identify clusters which have high disease risk